5592 results about "Time synchronization" patented technology

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The embodiment of the invention provides an industrial real-time data transmission guarantee method and system based on a TSN. The method is applied to the field of industrial communication and intelligent scheduling, and comprises the steps of obtaining industrial field network node information, completing TSN topology deployment and time synchronization, extracting communication characteristics and historical load data of various task flows, constructing a unified time sequence, and inputting a prediction model to generate a load prediction result. And in combination with task parameters, a scheduling strategy list and a prediction result, inputting a TSN digital twin network model to carry out analogue simulation, evaluating performance indexes under multiple strategies, selecting an optimal scheduling strategy and issuing the optimal scheduling strategy to TSN switching equipment, thereby realizing accurate scheduling control configuration of various task flows. According to the scheme, intelligent evaluation and automatic optimization issuing of the scheduling strategy are realized, the real-time performance and the deterministic communication capability of an industrial network are remarkably improved, the resource utilization efficiency and the adaptability of the scheduling decision are enhanced, and the key task flow is ensured to be reliably transmitted on time.

The invention provides an underwater multi-modal identification method and system for hidden micro defects of a high dam, and the method comprises the following steps: obtaining displacement monitoring data, stress monitoring data and temperature monitoring data, generating a multi-source monitoring data matrix through time synchronization processing, extracting abnormal features through employing a self-adaptive weight method, and carrying out the recognition of the abnormal features. Obtaining a suspicious area list with priority; planning a sonar scanning path according to the suspicious area list, obtaining edge feature data through multi-stage signal enhancement processing, and determining an accurate defect area; acoustic data and optical data are collected, and a fusion feature matrix is obtained through space-time registration and feature fusion; and carrying out feature discrimination on the fusion feature matrix, determining defect types and levels, obtaining a positioning result and a reliability evaluation result through multi-level analysis, and finally generating a defect identification report. According to the invention, the accuracy and reliability of underwater hidden micro defect identification are improved.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention relates to the technical field of data processing, and discloses a PTP network performance monitoring method and system based on big data analysis, and a medium. The method comprises the following steps: acquiring PTP message data, equipment state data and RTP stream data; preprocessing the data and calculating a clock error; constructing a time sequence database and an analysis model; real-time monitoring and anomaly detection are executed; performing performance scoring and fault positioning by applying machine learning; and carrying out network optimization and predictive maintenance. According to the PTP network performance monitoring method based on big data analysis, global state monitoring, intelligent anomaly detection, accurate fault positioning and predictive maintenance of the PTP network are achieved, and therefore the high-precision time synchronization requirement of a broadcast and television IP making and broadcasting system is guaranteed.

The invention provides a robot obstacle avoidance and navigation method based on multi-modal fusion and a visual language model, and the method comprises the steps: collecting different modal data in real time through a plurality of sensors, and carrying out time synchronization processing and normalization processing; extracting features of different modal data and fusing the features through an intermediate layer; and inputting the fused multi-modal data into a visual language model, generating a semantic map of the environment by utilizing semantic segmentation of the model and a target detection result, generating an action strategy in combination with a natural language instruction and a visual analysis result, and converting the generated action strategy into a control signal which can be executed by the robot to realize closed-loop control. According to the method, the visual language model and the multi-modal sensor fusion technology are combined, and the perception, decision making and real-time response capabilities of the mobile robot in a complex dynamic environment are improved.

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: collecting cable multi-physical field data; establishing a time synchronization mechanism according to the cable multi-physical field data to obtain a multi-physical field signal data set; extracting eigenentropy value characteristics of each physical field according to the multi-physical field signal data set; constructing an entropy feature vector based on the eigenentropy feature of each physical field; defining a cable initial state reference entropy according to the entropy feature vector; and based on pre-acquired historical data and the entropy value feature vector, quantifying the aging catalytic effect under the synergistic effect of the temperature field and the partial discharge field, identifying the microcrack propagation effect generated by the combined effect of the mechanical strain and the temperature field, and constructing an entropy value synergistic amplification coefficient. According to the method, the cooperative amplification coefficient is established based on the entropy theory, and the influence of the multi-physical field interaction on the cable aging process is accurately quantified.

The invention discloses a data acquisition method and system and a storage medium, and relates to the technical field of data acquisition and processing.According to the technical scheme, multiple sensor devices and data interfaces are integrated, multi-source data are acquired in real time, format standardization processing, time synchronization correction and spatial information alignment are carried out through a multi-mode fusion module, and the data acquisition efficiency is improved. Calculating to obtain a data consistency factor Tyhz and evaluating the data consistency factor Tyhz; when the data consistency factor Tyhz does not reach the standard, a data optimization module performs noise filtering and abnormal value elimination on a multi-source data set, and an intelligent analysis module calculates a risk prediction parameter Fcyz by using a convolutional neural network; the early warning evaluation module compares the Fcyz with a risk evaluation threshold Fth, calculates a risk early warning index Gyzs, compares the risk early warning index Gyzs with a risk early warning threshold E, and dynamically generates an early warning execution scheme, so that information pushing and emergency resource scheduling are realized, the problems of low multi-source data fusion efficiency and insufficient early warning precision are solved, and the safety of the system is improved. And the risk identification and emergency response capabilities of the urban emergency management system are effectively improved.

The invention relates to a network real-time synchronization communication system, and relates to the technical field of computer network communication. The system comprises four core modules: a hardware optical IO synchronization module which integrates a multi-wavelength optical transceiver array and an anti-jitter circuit and provides a nanosecond global clock signal, and the synchronization precision is less than or equal to 15ns; the time sequence arrangement module is used for dividing a fixed time window and a dynamic buffer window based on a time sensitive network, supporting a priority preemption mechanism of the SRIO bus and realizing deterministic data transmission; the distributed RTDATA nodes replace a traditional single-board computer, a heterogeneous computing unit and an intelligent storage controller are integrated, and protocol stack processing delay is reduced through the zero copy technology; according to the dynamic multicast routing system, a safely isolated multicast tree is constructed as required, and link load distribution is optimized in combination with machine learning. Through collaborative design of hardware optical synchronization and dynamic data arrangement, the problem of real-time performance reduction caused by multi-node communication concurrence is solved.

A system for the temporal ordering of multi-channel data streams, comprising the following: a multi-channel data reception module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect discrepancies in timestamps; a time synchronization module configured to align data packets using timestamp normalization, adaptive buffering, and AI-based predictive modeling; an AI-powered predictive modeling module trained to estimate missing or delayed timestamps and to reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adjust buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct order of the data packets and transmit them to a processing or analysis system; and a module for error detection and correction that is configured to detect inconsistencies in timestamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention provides a multi-modal sensor time synchronization method for road monitoring, which comprises the following steps: acquiring sensor data of target road detection, and determining communication time delay; the compensation value is optimized in real time according to the actual network condition and the environment change, and an optimized data set is obtained; determining a network topology structure based on the complete optimization data set; according to the optimized network topology structure, determining a time synchronization signal sequence of each node relative to the time delay information in the network; time synchronization of all sensors for target road detection is optimized. According to the invention, by introducing strategies of dynamic environment modeling and adaptive network topology optimization, the defects of the traditional time synchronization technology are effectively overcome, and high-precision multi-modal data time synchronization can be realized in a complex dynamic environment.

The invention relates to the technical field of synchronization in data communication, and provides a PTP protocol-based chip-level hardware timestamp generation method and system, and the method comprises the steps: 1, receiving a synchronization message and a delay request message from a main clock, carrying out the evaluation of a physical layer signal, and recording a first hardware timestamp; 2, responding to the received delay request message, generating and sending a delay response message, acquiring bidirectional transmission delay data by exchanging the delay request and the delay response message, and calculating clock skew in combination with the first hardware timestamp and the bidirectional transmission delay data; 3, adjusting the local clock frequency according to the clock skew; 4, after the synchronization is completed, associating the second hardware timestamp with the corresponding data message, and storing the associated data in a buffer area in the chip; and 5, generating standardized timestamp information, and outputting the standardized timestamp information through a chip interface. And high-precision time synchronization and timestamp generation are realized.

The invention relates to the technical field of edge calculation application and partial discharge detection, in particular to a partial discharge multichannel signal real-time synchronous acquisition method based on edge calculation. According to the method, the functions of multi-channel signal acquisition, time synchronization, feature analysis, hierarchical storage and the like are integrated at edge nodes, asynchronous acquisition, unified timestamp marking and data synchronous fusion of multiple types of partial discharge signals are realized, and extraction of multi-dimensional feature parameters such as time domain, frequency domain and energy and intelligent event discrimination are locally completed. For abnormal signals, the system realizes classified storage and remote uploading after encryption and compression processing; and for normal signals, dynamic management is carried out through circular caching. According to the method, the accuracy and efficiency of multichannel signal synchronous acquisition are remarkably improved, the data safety and the system adaptive capacity are enhanced, and the method is suitable for real-time online monitoring and intelligent diagnosis of partial discharge of power equipment.

The invention relates to the technical field of data processing, and discloses an automatic noise monitoring system and method based on multi-sensor data fusion. According to the system, a calibration module carries out time synchronization processing on noise data collected by multiple sensors; the extraction module adopts a singular value decomposition algorithm to extract frequency domain and time domain features; the separation module analyzes and separates traffic, construction and industrial noise sources through attention independent components; the construction module generates noise space propagation characteristics in combination with geographic information data; the classification module identifies the type of a noise source through a space-time convolutional network, locates coordinates and allocates responsibility weight. The technical problem that the existing noise monitoring technology cannot realize multi-source noise intelligent identification and pollution source accurate traceability is solved.

The invention provides an electric two-wheeled vehicle collision detection and early warning method and related equipment, and the method comprises the steps: obtaining environment information in front of and behind a vehicle, the environment information comprising point cloud data collected by a radar sensor and image data collected by a camera; performing time synchronization and space coordinate conversion processing on the point cloud data and the image data, and constructing a sensing data frame under unified space-time reference; based on the sensing data frame, radar features and visual features of the target object are extracted respectively, feature fusion is carried out to form a joint feature vector, target matching and association are carried out based on the joint feature vector, and a fused obstacle sensing result is generated; and based on the obstacle sensing result, in combination with the current motion state of the vehicle, predicting and evaluating a potential collision risk, determining a collision risk level, and executing a corresponding early warning prompt according to the collision risk level. And the driving safety and the system response efficiency of the electric two-wheeled vehicle in a complex traffic environment are integrally improved.

The invention relates to the technical field of mining area geological monitoring, and discloses a dynamic monitoring system for mining area geological stability, which comprises a data acquisition module used for acquiring geological monitoring data of a plurality of data sources in a mining area; the data preprocessing module is used for performing time synchronization, space alignment, data cleaning and standardization processing on the acquired data; the long-period trend analysis module is used for carrying out time sequence modeling and multi-scale change rate analysis on the preprocessed data; and the adaptive risk model module is used for setting and dynamically adjusting a risk threshold based on the long-period trend analysis result. By introducing a long-period trend analysis and change rate evaluation technology, the recognition precision and early warning foresight of the slow change of the geological structure in the mining area are remarkably improved, and long-term accumulated change characteristics such as settlement and inclination can be captured; meanwhile, the self-adaptive adjustment of the alarm threshold is realized by using the dynamic risk model, so that the system can sensitively deal with geological states at different stages.

The invention relates to the technical field of intelligent dehumidification, in particular to a dehumidification control method and system based on humidity detection and an intelligent dehumidifier, and the method comprises the following steps: collecting outdoor humidity data and indoor humidity data through a humidity sensor, executing time synchronization and marking, and carrying out data normalization processing to obtain initial environment humidity detection data; according to the invention, through humidity detection, advanced data processing and trend prediction, the precision and foresight of dehumidification control are improved, subtle humidity change is identified by using a difference technology, real-time fluctuation of environment humidity is accurately reflected, the humidity change in a short period is predicted by using a linear regression model, the accuracy of a prediction result is improved, prediction errors are reduced, and the method is suitable for large-scale popularization and application. By means of the calculation result of the humidity setting difference of the different areas, accurate adjustment, continuous monitoring and dynamic adjustment of dehumidification parameters of the dehumidification equipment in the different areas are achieved, it is ensured that the equipment is kept in the optimal operation state all the time, the energy efficiency is remarkably improved, and the comfort and safety of the living and working environment are improved.

The invention discloses a multi-modal data fusion power equipment health state diagnosis method and system, and belongs to the field of power equipment state monitoring, and the method comprises the steps: S100, collecting infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals of power equipment, and carrying out the time synchronization and space registration; and S200, acquiring infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals, and inputting the infrared thermal imaging data, the vibration signals, the current harmonic data and the partial discharge signals into the dynamic weight fusion model to obtain an equipment health state score and a fault type. And S300, according to the equipment health state score and the fault type, triggering grading alarm. According to the invention, timely early warning can be carried out on potential fault hidden dangers of power equipment.

The invention relates to the technical field of mine safety production analysis, and discloses a mine safety production data fusion analysis method based on multi-source perception, which comprises the following steps: step 1, carrying out real-time data acquisition on physical parameters related to safety production in a mine environment through various types of sensors arranged in the mine environment, the collected data are stored as independent data streams with timestamps, and the data of different sensors have time inconsistency according to the difference set by the sampling frequency and the clock; and step 2, carrying out time delay compensation processing on the multi-source data with the timestamps acquired in the step 1. An optimization model is established in combination with a variational method to perform time delay compensation and time synchronization processing on sensor data, the problem of time inconsistency caused by different sampling frequencies and clocks of sensors is eliminated, fusion processing of multi-source sensor data on a unified time reference is realized, and an accurate data basis with consistent time sequence is obtained.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The embodiment of the invention discloses a low-orbit navigation enhanced transmitting terminal time difference calibration method, device and equipment and a medium, and relates to the technical field of satellite navigation, and the method comprises the steps: obtaining a low-orbit satellite satellite-borne receiver clock error, ground observation station coordinates, an observation station receiver clock error and other parameters through calculation of observation data of a GNSS satellite; the ground observation station calculates an inter-system deviation between a low earth orbit satellite system corresponding to each low earth orbit satellite and a GNSS system according to the downlink navigation data of each low earth orbit satellite; and selecting one low-orbit satellite as a reference satellite, obtaining the deviation between reference systems, calculating the time deviation of the navigation enhancement transmitting end of the deviation between other systems and the deviation between the reference systems, and compensating the clock error of the corresponding low-orbit satellite based on the parameter. The method can be used for calibrating the relative time difference of each low-orbit satellite navigation signal transmitting end, realizes low-orbit navigation signal time synchronization, is beneficial to improving the positioning, navigation and time service capabilities of a low-orbit satellite system, and provides more accurate services for users.

The invention provides a power line fault distance measurement system and method, and belongs to the technical field of power line detection. The system comprises monitoring terminals deployed at two ends of a line and a background processing system, and each monitoring terminal comprises a sensor module used for detecting a traveling wave current signal related to a power line fault in the line; the data acquisition module is used for extracting the arrival time of a traveling wave head according to the detected traveling wave current signal; the GPS time synchronization module is used for adjusting the local clock of the data acquisition module, reducing the time synchronization error of the two monitoring terminals and correcting the arrival time of the traveling wave head; and the background processing system is used for calculating a traveling wave time difference and a junction wave velocity normalization processing result according to the corrected arrival time at the two ends of the line, and determining a fault position coordinate and a fault type. According to the invention, collaborative optimization of fault positioning precision and type identification in a hybrid power line environment is realized.

The invention discloses a super-long water tunnel defect underwater positioning device and method, and the method comprises the steps: obtaining multi-source sensor data of an underwater vehicle, and carrying out the time synchronization and preprocessing of the data; establishing an acoustic environment feature model, identifying strong reflective identification information, and updating environment parameters; the dynamic weight of each data source is calculated, data consistency check is carried out, and multi-source data fusion is executed; performing trajectory optimization on a fusion positioning result, executing identification point constraint processing, and performing backtracking correction on a historical trajectory; and performing multiple verification and uncertainty evaluation on the positioning result. According to the method, through multi-source data fusion and a multi-level optimization strategy, the problems of multipath effect, thermocline influence, inertial navigation accumulative errors and image quality in a complex underwater environment are effectively solved, and high-precision positioning in the ultra-long water delivery tunnel is realized.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention relates to the technical field of soft robots and intelligent control, in particular to an intelligent control method based on the Internet of Things. The method comprises the following steps: carrying out multi-modal data acquisition and preprocessing on a contact process of a gripper and an object to obtain a time synchronization data set; constructing a tactile feature tensor including pressure, friction force and strain force according to the time synchronization data set; performing stress gradient calculation and mapping according to the tactile feature tensor to obtain a stress gradient field and a region threshold mapping table; performing stress gradient threshold adaptive adjustment according to the regional threshold mapping table and the stress gradient field to obtain an adaptive threshold distribution map; and performing stress risk prediction according to the adaptive threshold distribution map to obtain a stress risk map. According to the method, the potential damage risk is predicted by sensing the contact state of the gripper and stress concentration, the adaptive capacity, robustness and success rate of the grabbing process are remarkably improved, and the method is particularly suitable for grabbing tasks of fragile and irregular objects which are difficult to prejudge.

The invention relates to the technical field of software time synchronization, and discloses a software time synchronization method and system for multi-sensor data fusion, and the method comprises the steps: extracting temperature, load and drift frequency characteristics through principal component analysis based on the working state and historical drift data of a sensor, and constructing a confidence evaluation model to calculate the credibility of a timestamp; identifying an abnormal node group by using k-means and an isolated forest algorithm, analyzing a phase deviation fluctuation and network delay interaction effect, extracting a nonlinear drift feature in combination with a Prophet algorithm, and calculating a phase correlation value by using Hilbert cross-correlation; and dynamically adjusting node clock parameters and generating a calibration timestamp according to the network influence weight and the stability evaluation result. According to the method, the problem of time desynchrony caused by clock drift and network delay factors in a distributed system is effectively solved, and the overall time consistency and reliability of the system are improved.

The invention discloses a robot grinding real-time defect detection and self-adaptive correction system based on deep learning. The problem that in the prior art, real-time detection, defect correction and accurate path optimization are difficult to achieve in the grinding process is solved. The image, the three-dimensional point cloud and the moment data are fused through a multi-modal sensing system, and data fusion processing is performed by adopting a time synchronization mechanism, so that high-precision defect detection is realized. And the self-adaptive correction module dynamically adjusts the pressure, the speed and the angle of the grinding tool according to real-time feedback by using a reinforcement learning algorithm, so that accurate grinding for different workpiece surfaces is ensured. The system is further combined with closed-loop feedback control, the grinding path is continuously optimized, and the consistency and stability of the grinding quality are improved. Compared with the prior art, efficient and automatic defect detection and self-adaptive correction can be achieved on the complex and irregular workpiece surface, and the production efficiency and the surface treatment quality are remarkably improved.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.